Accuracy of administrative coding in identifying hip and knee primary replacements and revisions
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Hospital discharge notes can be used to identify rates of revisions in hip and knee arthroplasty surgeries if such administrative codes are accurate. In order to trust the data taken from the hospital discharge abstracts it is important to assess their reliability. The purpose of this study is to evaluate the accuracy of the administrative coding used in measuring revision rates for total hip and knee arthroplasty. METHODS: Validation coding was determined by two orthopaedic surgery residents who reviewed the operative, radiological and discharge summaries in order to identify the revision rates for total hip and knee arthroplasty. A random sample of 637 patients from two tertiary care hospitals was studied. These patients had total hip or knee arthroplasty between 1996 and 2006. All of these patients had an International Classification of Disease (ICD)-9CM or ICD-10CM code indicating what procedure they had done. The validation reviewers were blinded to the administrative codes used. The sensitivity, specificity and positive and negative predictive values of the administrative codes for revision rates were measured. RESULTS: Based on 1201 procedures performed on 637 patients, when comparing validation review versus hospital administrative chart coding for primary and revision surgeries of total hip and knee arthroplasty, the following data were obtained: for total hip arthroplasty sensitivity is 99%, specificity is 91%, positive predictive value is 91% and negative predictive value is 99%; for total knee arthroplasty sensitivity is 89%, specificity is 98%, positive predictive value is 97% and negative predictive value is 93%. The accuracy of ICD-9CM and ICD-10CM were 96% and 95%, respectively. CONCLUSION: This study demonstrates that ICD-9CM and ICD-10CM codes can be used accurately when analysing hip and knee arthroplasty. This study was conducted in a large tertiary academic centre where a significant number of records analysts are employed; therefore, there should be little inter-hospital error. These results should help researchers understand the potential accuracy of classification for these procedures as part of an audit or quality assurance project.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.169 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".